By Carlos A. Coello Coello, Daniel Cortés Rivera, Nareli Cruz Cortés (auth.), Jon Timmis, Peter J. Bentley, Emma Hart (eds.)
In many ways, our immune platforms are as complicated as our brains. They study, are expecting, bear in mind and adapt, preserving us from the maelstrom of pathogens that infect us day-by-day. laptop technology usually takes concept from the doubtless unending functions of usual platforms. it's going to hence be no s- prise that, just like the ?eld of Arti?cial Neural Networks encouraged from brains, we have a energetic ?eld of study referred to as Arti?cial Immune structures (AIS), encouraged by means of our personal immune platforms. even if nonetheless particularly new, the former 10 years has visible the paradigm of AIS quickly identify itself as an enormous organic metaphor. Researchers worldwide fruitfully take advantage of “immunological rules” in lots of di?erent how one can offer mechanisms for tackling a wide selection of functions. during this quantity we current the court cases of ICARIS 2003, the 2d Int- nationwide convention on Arti?cial Immune structures. This was once the second one int- nationwide convention completely devoted to the ?eld, and the tremendous profitable ?rst convention held in Canterbury, united kingdom in 2002. The quantity and - versityofpapersinthisyear’sconferenceisatributetotheever-growingnumber ofresearchersinthearea,andrepresentativeofthesolidfoundationofworkthat now exists during this sector. the variety of themes thought of is vast. for instance, at one finish of the spectrum we see a range of papers delivering an important t- oretical grounding for the ?eld. on the different finish, now we have an exhilarating variety of purposes to real-world difficulties, overlaying, for instance, job-shop scheduling and fault detection in refrigeration systems.
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Extra info for Artificial Immune Systems: Second International Conference, ICARIS 2003, Edinburgh, UK, September 1-3, 2003. Proceedings
Thesis, Computer Science, University of Washington, Seattle, WA, USA. 19. Zahn, C. T. (1971), “Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters”, IEEE Trans. 68-86. An Investigation of the Negative Selection Algorithm for Fault Detection in Refrigeration Systems Dan W. Taylor1,2 and David W. uk Abstract. Failure of refrigerated cabinets costs millions annually to supermarkets, and a large market exists for systems which can predict such failures. Previous work, now moving towards deployment, has used neural networks to predict volumes of alarms from refrigeration system controllers, and also to predict likely refrigerant gas loss.
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In order to perform knowledge extraction, a fundamental step in the analysis consists of identifying groups of genes that manifest similar expression profiles. This way, it is possible to explore the inherent structure of the data, thus discovering possible correlations among genes and revealing hidden patterns. This problem is commonly known as gene expression data clustering, and it has already been treated by several different techniques. The commonly used approaches are hierarchical clustering , self-organizing maps (SOM) , principal component analysis (PCA)  and k-means clustering .
Artificial Immune Systems: Second International Conference, ICARIS 2003, Edinburgh, UK, September 1-3, 2003. Proceedings by Carlos A. Coello Coello, Daniel Cortés Rivera, Nareli Cruz Cortés (auth.), Jon Timmis, Peter J. Bentley, Emma Hart (eds.)